Method for automatically adapting an image data set obtained by an x-ray device

ABSTRACT

A method and system for automatically adapting an image data set obtained by an X-ray device. Binary masks are generated for single images of the image data set. The masks differentiate different components of an imaged target object from each other. A reference image of the image data set is determined therefrom, that contains a largest cohesive region of one of the components. From a mean value determined for the region and a specified target value, an offset is determined in such a way that by applying it to the pixel values used for determining the mean value. The pixel values are adapted in such a way that a mean value of the pixel values adapted by the offset corresponds to the specified target value. The determined offset is then applied to all pixel values of the image data set in order to adapt the image data set.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of EP18178807.6, filed on Jun. 20,2018, which is hereby incorporated by reference in its entirety.

FIELD

Embodiments relate to a method for automatically adapting a medicalimage data set,

BACKGROUND

Current methods and X-ray devices provide high-quality imaging ofindividual target objects or patients. However, when imaging a pluralityof different patients, different pixel or intensity values neverthelessresult in the corresponding X-ray images for the same types of tissue.This may be caused, for example by individual properties of therespectively used X-ray device, such as, for instance a different orinaccurate calibration or different frequencies or spectra of the X-rayradiation used. If, in practice, the X-ray radiation used is notactually monochromatic, then this may lead to beam hardening and/orother non-linear effects, that may ultimately affect obtained imagedata. Due to these differences between scans of different patientsand/or scans of the same patient by different X-ray devices, a reliableand consistent application of standardized processing or evaluationmethods to the obtained X-ray images or image data may be rendereddifficult.

BRIEF SUMMARY AND DESCRIPTION

The scope of the present invention is defined solely by the appendedclaims and is not affected to any degree by the statements within thesummary. The present embodiments may obviate one or more of thedrawbacks or limitations in the related art.

Embodiments improve consistency and usability of X-ray image data.

A method is used for automatically adapting an image data set obtainedby an X-ray device, that maps a target object. The image data set isacquired in one method step of the method. The image data set includes aplurality of single images, that map different slices or planes of thetarget object or correspond thereto. The image data set may be orconstitute a volume reconstructed using known methods. Acquisition ofthe image data set may include imaging the target object by the X-raydevice. Acquisition of the image data set may also retrieve the imagedata set, for example, from a data memory or a corresponding data memoryon which the image data set is stored, supplies it to a correspondingdata processing facility adapted for carrying out the method and isrecognized or read in by the facility. The target object may ultimatelybe any object or any article, but it may be a patient or a section of apatient, for example a particular organ or a region of tissue. The imagedata set maps, for example, a specific target tissue or a specifictarget tissue type, for example brain tissue, of the target object, thatis of interest, for example for an examination, assessment or diagnosisof the patient respectively.

In a further act, a respective binary mask, e.g. a respective binarymask image, is generated for each of the single images using at leastone specified threshold value. The binary masks differentiate differentcomponents of the target object in the single images from each other. Abinary mask is an image associated with the respective single image oran amount of data associated with the respective single image, thatincludes or contains only two different values. The respective binarymask indicates pixel by pixel or pixel accurately for the respectivesingle image whether the respective pixel maps a particular component ortissue type of the target object or not. A value on the Hounsfield scalemay be specified as the specified threshold value, and may lie forinstance between typical values for bone and for soft tissue. Togenerate the respective binary mask, a check is then made for each pixelof the respective single image as to whether the pixel's intensity orbrightness value lies above or below the specified threshold value onthe Hounsfield scale, in other words in Hounsfield units (HU). Dependingon the result, one or the other of the two binary values, for example 1or 0, is defined for a respective corresponding pixel of the binarymask. By way of the binary mask an initially rough distinction maytherefore be made between the different components or types of materialof the target object.

In a further method act, using all of the binary masks, that one of thesingle images is determined that contains the largest cohesive region ofone of the components, for example, of a specified component. Thedetermined single image is determined or selects as the reference imagefor the further method. The reference image is therefore one of thesingle images, in which the pixels associated with the correspondingcomponent or mapping the component form the largest cohesive region orgrouping. A cohesive region is a group or quantity of pixels of one ofthe single images respectively. All pixels of the cohesive regioninclude intensity or Hounsfield values, that lie on the same side of thethreshold value used to generate the binary masks. Furthermore, everyother pixel of the cohesive region may be reached from every pixel ofthe cohesive region, in which a jump is made as often as necessary to animmediately adjacent pixel of the cohesive region. Every pixel of thecohesive region is therefore connected to a remainder of the cohesiveregion by pixels of the cohesive region. A cohesive region is definedfor one of the binary masks respectively, where all pixels of a cohesiveregion include the same of the two binary values of the mask.

In a further method act of the method a mean value of all pixel valuesof the reference image pertaining to the largest cohesive region isdetermined. The mean value of all pixel values, that is to say intensityor HU values, of the pixels of the reference image that form thedetermined largest cohesive region is calculated.

In a further method act of the method an offset is determined orcalculated from the determined mean value and a specified target value.The offset is determined such that by applying the offset to the pixelvalues used for determining the mean value, e.g. the pixels of thedetermined largest cohesive region. The pixels are adapted in such a waythat a mean value of the pixel values of the pixels adapted by theoffset corresponds to the specified target value. All pixels or pixelvalues of the cohesive region may be adapted in the same way by theoffset. It may be specified how the offset should be applied to thepixels or pixel values, for example in the form of or according to aspecified mathematical equation, linking or function. The target valuemay be predefined specifically for at least one of the componentsdifferentiated or distinguished by the specified threshold value or thebinary masks. For example, a particular CT number or a particular HUvalue, in other words a particular value on the Hounsfield scale, may bespecified as the target value. Depending on the kind or type ofrespective component, the target value may be for example an empiricalvalue or a typical value from previous image data sets of other targetobjects.

In a further method step of the method the determined offset foradapting the image data set is applied to all pixel values or pixels ofthe image data set. All pixels or pixel values of all single images ofthe image data set are adapted in the same way, therefore. Theadaptation occurs according to the mathematical function, dependency orequation that is also used as a basis when determining the offset.

The image data set may be automatically adapted or corrected by themethod such that consistent imaging or mapping of the target objectreliably results. This is made possible by the dynamic and individualdetermination of the offset for the respective image data setirrespective of with which X-ray device the target object was imaged. Anormalization of the intensity or HU values of the image data set isachieved by the method. As a result, image data sets of different targetobjects may be compared with each other consistently, reliably andmeaningfully, for example, when the image data sets map the samecomponents, in other words target tissue or target tissue types. Theabsolute HU values of the image data set are adapted by the method insuch a way therefore that the image data set may automatically then beconsistently and correctly processed further by additional algorithms ormethods, that are based on HU threshold values or distinguish differentcomponents or tissue types by specified or predetermined thresholdvalues. Without the application of the method the differences that maybe observed with different target objects or patients and/or withdifferent X-ray devices or calibrations may lead to inconsistencies orerrors in the application of such HU threshold value methods oralgorithms. It is only due to the method that consistently automaticprocessing of image data sets is reliably made possible, therefore.

The terms “pixel” and “pixel value” may refer, for example, to 2D pixelsor to 3D voxels.

In an embodiment, for determining the reference image, a respectivelargest cohesive region for the corresponding component, or the largestcohesive component, is individually determined for each of the binarymasks and then the respective determined largest cohesive regions of allof the binary masks are compared with each other. A respective number ofthe pixels or values of the respective largest cohesive regions of allbinary masks may be determined and compared with each other. Onecohesive region is therefore larger than another cohesive region if itincludes a greater number of pixels or voxels. As a result, one of thebinary masks is determined therefore, which, of all of the binary masks,includes the largest cohesive region of a particular one of the twobinary values. The reference image is then that one of the single imagesassociated with the determined binary mask, that one of the singleimages from which the respective determined binary mask has beengenerated. Since the binary masks are much less complex compared to theunderlying single images, the method may be applied quickly and with lowcomputing effort which supports a real-time application of the method.

In an embodiment, two different CT numbers are specified as thethreshold values for generating the binary masks, by which numbers, airis differentiated from soft tissue and soft tissue is differentiatedfrom bone. For generating the binary masks from the single images, thepixels thereof, for pixel values lying between the two specified CTnumbers, a first value is allocated to the binary mask, and for pixelvalues, that lie below the smaller or above the larger of the two CTnumbers, a second value is allocated to the binary mask. The twospecified CT numbers, that is to say HU or X-ray damping values,distinguish or differentiate three different components of thecategories of components of the target object. However, two of thecomponents or categories are allocated the same value in the binarymask. The soft tissue may be differentiated or isolated particularlyaccurately and reliably hereby. By definition air has a CT number of−1,000 HU and water a CT number of 0 HU. Fatty tissue may include, forexample, a CT number of −100 HU, while bones, depending on density, mayinclude, for example, a CT number between 500 HU and 1,500 HU.Preferably, the lower of the two specified CT numbers may therefore lie,for example, between −500 HU and −150 HU and the larger of the twospecified CT numbers between 50 HU and 400 HU. The soft tissue may bereliably distinguished hereby, in other words differentiated, from airand bone regions despite the described potential differences betweendifferent target objects and/or X-ray devices.

In an embodiment, a low-pass filter, e.g. a 3D low-pass filter, isapplied to the acquired image data set before generation of the binarymasks. A limit frequency or time constant of the low-pass filter may bespecified as required or depending on the application or may beautomatically or dynamically determined. A noise reduction in the imagedata set may be achieved by applying, the low-pass filter. Ultimately,an improved image quality of the adapted image data set may be achievedthereby. In addition, a more reliable differentiation of the variouscomponents of the target object by way of the binary masks mayoptionally be achieved.

In an embodiment, after generation of the binary masks, an erosionoperation is applied to the binary masks before the reference image isdetermined. Due to the erosion operation, values of isolated pixels ineach of the binary masks are set at the other one of the two binaryvalues or mask values respectively. If, for example, one of the binarymasks includes a cohesive region comprising pixels including the binarymask value 0 and located within the cohesive region is a single, e.g.isolated, pixel including the binary mask value 1, then, due to theerosion operation, the binary mask value of the single isolated pixel isset from 1 to 0. This takes account of the knowledge that isolatedpixels represent either an image artifact or are irrelevant to theevaluation or diagnosis of the target object.

An isolated pixel is a pixel that within the respective binary mask issurrounded on all sides by pixels that include the other one of the twobinary values respectively. Similarly, a number threshold value may bespecified for a number or quantity of pixels, whose values are set atthe other value respectively if the pixels are surrounded on all sidesby pixels including the other binary value respectively. For example,two or more adjacent pixels including the same binary value may beregarded and treated as isolated pixels if the two or more adjacentpixels are surrounded by pixels of the other binary value respectively.

Due to the erosion operation, a distribution of the binary values withinthe respective binary masks is therefore smoothed. As a result,computing effort and time expenditure when determining the largestcohesive region(s) may be reduced and the method accelerated, without avalidity of the resulting adapted image data set being limited hereby.In a specific application, due to the erosion operation, for examplebrain tissue may be differentiated from the scalp and/or neck tissue.

In an embodiment for determining the offset, the specified target valueis subtracted from the determined mean value. For adapting the imagedata set, the determined offset is subtracted from all pixel values ofthe image data set. The offset may be signed, so in the case of anegative offset, the subtraction effectively corresponds to an additionof an amount of the determined offset to the respective pixel values.Adaptation of the image data set may be carried out easily, quickly andin an understandable way. Because the offset is applied to all pixelvalues of the image data set in the same way, a ratio, for example acontrast ratio, is retained or maintained between different pixels orcomponents of the original image data set even in the adapted image dataset. Therefore, no image information is lost due to the adaptation ofthe image data set.

In an embodiment the target object is automatically recognized by anobject recognition algorithm As a function of the recognized targetobject or type, the target value for determining the offset is thenautomatically selected from a plurality of target values specified fordifferent target objects or different types of target objects.Recognition of the target object may lead to that by the objectrecognition algorithm, e.g. by automatic processing of the image dataset, the kind or the type or the like of the target object isdetermined, for example in other words which body part or which organ ofthe image data set is imaged. As a result, the suitable target value maybe reliably automatically determined or selected. The object recognitionor image processing algorithm may be carried out or implemented forexample by a neural network. The method may be automated further by theautomatic recognition of the target object, whereby an error rate, forexample due to avoidance of operating or input errors, may be reduced.

Recognition of the target object may similarly also include anindividual identification. For example, the individual differences,already discussed, for different target objects, e.g. patients may betaken into account. The respective target object, e.g. respectivepatient, may be examined or imaged several times. The threshold valuefor generating the binary masks may likewise be determined or selectedas a function of the automatically recognized target object or anidentity. As a result, the different components of the target object inthe single images may be reliably distinguished or differentiated fromeach other since, for example, as described, individual properties ofthe respective target object may be taken into account.

In an embodiment, using the adapted image data set and at least onelikewise correspondingly adapted further image data set, adifferentiation threshold value is automatically determined thatprovides a clear distinguishing or differentiation of two components ofthe target objects. The adapted image data set and the at least onefurther image data set image map similar target objects. A plurality ofimage data sets of different but similar target objects are evaluated todetermine the differential threshold value. The differential thresholdvalue may then be consistently and reliably used by further automaticimage processing or image evaluation algorithms or methods. Thedifferentiation threshold value may then be reliably used also for otherdata sets of corresponding target objects to distinguish respectivecomponents therein from each other.

Target objects are similar if, for example, the target objects map thesame body part or the same organ or the same tissue types of the varioustarget objects or patients.

In an embodiment, a head of a patient is at least partially mapped bythe image data set as the target object. The adapted image data set andthe differentiation threshold value are supplied as input data to animage processing algorithm. The image processing algorithm automaticallydetermines on the basis of the differentiation threshold value and usingthe adapted image data set automatically an infarct core volume presentin the head. The method may be applied with DynaCT scans. For automaticdetermination of the infarct core volume the image processing algorithmis reliant on reliable, absolute HU values in the underlying adaptedimage data set. The absolute HU values are reliably and consistentlyadjusted by the method for different target objects and at differentcalibrations of the X-ray device. By way of the method, the targetobject may be mapped, e.g. acquire the image data set, and also toevaluate the image data set automatically. Consequently, the evaluationmay be carried out quickly, consistently and with reduced error rates.

An embodiment provides a computer program that encodes or represents themethod steps of at least one embodiment of the method for automaticallyadapting an image data set obtained by an X-ray device. The computerprogram for carrying out the method steps or the method by the X-raydevice may be loaded in a data memory of a control device of the X-raydevice. The computer program may include programming to carry out themethod when the computer program is carried out by the control device.

An embodiment provides an electronically readable, data memory or datacarrier for a control device of an X-ray device. A program code isstored in the data memory, that includes at least one computer program.In addition, further control instructions for the control device and/orthe X-ray device may be stored or encoded in the data memory, e.g. aspart of the program code. The program code stored on the data memory isconfigured to bring about at least one embodiment of the method when thedata memory is used in the control device of the X-ray device and whenthe program code is executed by the control device, for example, by amicroprocessor and/or microcontroller of the control device.

An embodiment provides an X-ray device, for example, a computedtomography device (CT device), including an acquisition facility foracquiring a plurality of single images of a target object, that mapdifferent slices or planes of the target object and together form animage data set. The X-ray device includes a data processing facility forautomatically processing the acquired image data set. The X-ray device,e.g. data processing facility, is configured for automatically carryingout at least one embodiment of the method for automatically adapting theimage data set obtained or acquired by the X-ray device. The X-raydevice may include some or all of the facilities, components and/orproperties described in connection with the other aspects, including forexample, the acquisition facility for automatic recognition of thetarget object.

The data processing facility of the X-ray device may include a datamemory with a program code, that encodes or represents the method stepsof the corresponding method. The program code may be the computerprogram or include it. Furthermore, the data processing facilityincludes a processor connected to the data memory, for example amicroprocessor, that is arranged and adapted to execute program codestored on the data memory and thereby carry out the correspondingmethod. The X-ray device may be configured to carry out or record anangiography.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts a schematic flowchart of a method for automaticallyadapting an image data set obtained by an X-ray device according to anembodiment.

FIG. 2 depicts a single image of the image data set before theadaptation thereof according to an embodiment.

FIG. 3 depicts the single image of FIG. 3 after the adaptation of theimage data set according to an embodiment.

FIG. 4 depicts pixels of a single image of the image data set associatedwith a first tissue component according to an embodiment.

FIG. 5 depicts a distribution of HU values, determined from un-adaptedimage data sets, for two different tissue types of a plurality ofdifferent patients according to an embodiment.

FIG. 6 depicts a distribution of HU values, determined from adaptedimage data sets, for two different tissue types of different patientsaccording to an embodiment.

DETAILED DESCRIPTION

FIG. 1 depicts a schematic flowchart 1 of a method for automaticallyadapting an image data set obtained by an X-ray device, that maps atarget object.

The method includes a method step S1. The corresponding X-ray device maybe activated and the target object imaged for acquisition of the imagedata set. The image data set includes a plurality of single images, thatcorrespond to different slices of the target object. FIG. 2 depicts asingle image 2 of the image data set, with a head 3 of a patient beingmapped as the target object. At least two different tissue types orcomponents are imaged including a brain as the first component 4 and askull or bone tissue as the second component 5. The single image 2 ispart of the original, in other words un-adapted, image data set.

A high-quality display, for example of both soft tissue as well as adynamic perfusion in the brain tissue, is possible by way of anavailable X-ray devices, for example an angiograph system. However,absolute intensity or HU values of corresponding scans or images areless reliable than, for example, X-ray images obtained by a Helix CTmethod. Therefore, for example absolute HU values may differsignificantly from each other, for instance for a cerebrospinal fluid(CSF) or a white brain substance, in corresponding images of differentpatients. This is a problem against the background that currentlyavailable software, for example for automated calculation of infarctvolumes on perfusion scans, uses methods, that are based on HU thresholdvalues, using which, for example, the CSF is detected or distinguishedfrom other tissue types. It is only with reliable detection that theimage region that maps the CSF may then actually consistently berejected or ignored on calculation of the infarct volume.

One result of the adaptation of the image data set by the methodexplained in more detail below is shown in FIG. 3 in the form of anadapted single image 6. The adapted single image 6 results from theapplication of the method to the single image 2 shown in FIG. 2. Theadapted single image 6 shows the head 3 with the two components 4, 5,albeit with, compared to the single image 2, adapted or normalized HUvalues, in other words brightness or intensity values.

To obtain the adapted single image 6 from the un-adapted single image 2,a 3D low-pass filter for initial noise reduction is applied to theacquired un-adapted image data set in a method step S2.

In a method step S3 respective initial binary masks are extracted orgenerated from the single images of the image data set using specifiedthreshold values. The masks differentiate soft tissue from air and bonein the single images, for example, the first component 4 from the secondcomponent 5. Each pixel of the single images is allocated one of twobinary values respectively, from which the binary masks are formed, as afunction of whether the HU value of the respective pixel lies forexample between the specified threshold values or outside of thespecified threshold values or outside of an interval defined by thespecified threshold values. A binary mask determined from a particularsingle image includes the same number and arrangement of pixels as thesingle image underlying the respective binary mask. Since the respectivebinary mask is generated alternately or pixel accurately from therespective underlying single image, there is a clear association betweenpixels of the single image and the pixels of the associated binary maskgenerated from the single image.

Since the image data set may be a reconstructed volume, pixels may alsoinclude voxels of the single images or of the image data set.

An erosion operation is applied to the binary masks in a method step S4.As a result, pixels isolated in the binary masks are adapted torespective surrounding pixels, so ultimately only larger cohesiveregions remain or are left in the binary masks. Which pixels or pixelregions are treated as isolated, and are accordingly changed in value,may be determined by a specified number threshold value, that indicatesthe corresponding maximum number of cohesive pixels, that are stillconsidered isolated. By the application of the erosion operation, forexample pixels or image regions of such pixels or image regions that mapor represent the brain tissue, e.g. in a cerebellum, may be separated ordifferentiated, that represent or map a scalp or neck tissue.

A respective largest cohesive image and pixel region is determined foreach of the binary masks in a method step S5. Due to the underlyingthreshold value and the erosion operation, the pixels that include thesame pixel or binary value in the respective binary mask correspond tothe same component of the patient. For example, pixels or regions of oneof the binary masks, that include the pixel or binary value 1,correspond to the brain tissue, in other words the first component 4,while pixels or regions of the binary mask, that include the pixel orbinary value 0, correspond to other tissue types, for instance thesecond component 5. In method step S5 the corresponding largest cohesivepixel or image regions are determined for all binary masks for aspecified binary value or for a specified component—here for the firstcomponent 4—which regions in the underlying single image respectivelytherefore correspond to the largest cohesive regions of the respectivecomponent or tissue types.

In a method step S6, using the binary masks or using the largestcohesive regions determined for them, that one of the single images isdetermined in which, of all the single images, the largest cohesiveregion of the specified component is contained or displayed. One of themapped slices including the most pixels or voxels pertaining to thecorresponding component, here for example to the brain tissue, isdetermined. The associated single image is then determined or defined asa reference image.

In a method step S7 the pixels of the reference image, corresponding tothe determined largest cohesive region of the binary mask associatedwith the reference image, and their mean value, e.g. the average HUvalue, are calculated. FIG. 4 depicts the pixels selected fordetermining the mean value. FIG. 4 depicts only the first component 4,but not the second component 5. The mean value of the pixel or HU valuesof the pixels pertaining to the first component 4 is here approximately83 HU.

In a method step S8 an offset is calculated using the mean valuecalculated in method step S7 and a target value specified here for thefirst component 4. The specified target value is subtracted from thecalculated mean value. The offset is determined and calculated suchthat, after a mathematical subtraction of the offset, a resulting newmean value of the pixels or pixel values used in method step S6,depicted in FIG. 4, corresponds to the specified target value—of here,for example, 30 HU.

In a method step S9, for adapting the image data set, the determinedoffset is subtracted from all pixel or voxel values of the entireun-adapted image data set, e.g. all single images.

The adapted image data set is supplied together with at least onecorrespondingly adapted further image data set 7 to a data processingfacility.

FIG. 5 depicts a distribution 8 of HU values for two different tissuetypes of a plurality of different patients determined from correspondingun-adapted image data sets. HU values are plotted on an X axis 9. Forthe example, the CSF was used as the first tissue type and the braintissue as the second tissue type. Values or data points pertaining tothe CSF are identified by “o” and values or data points pertaining tothe brain tissue by “x”. The individual values or data points correspondto HU means in, similar, tissue types or regions of interest (ROI) ofthe different patients. A value pair from a value for the CSF and avalue for the brain tissue is plotted for each patient. Owing to theindividual differences in the patients and/or the acquisition conditionsof the underlying image data sets, the data points pertaining to thedifferent tissue types, in other words components, cannot bedifferentiated from each other by a single absolute threshold value.There is therefore no single HU value, by which the data points for thetwo components are distributed into two uniform groups, for which it istherefore true that all data points pertaining to the one component lieabove and all data points pertaining to the other component lie belowthe HU value.

In a method step S10 the data processing facility determines from thesupplied image data sets a differentiation threshold value 11 (see FIG.6), by which a clear differentiation of the data points pertaining tothe two different tissue types is possible on the basis the adaptedimage data sets. FIG. 6 shows in this regard an adapted distribution 10of the HU values from FIG. 5. The adapted distribution 10 results byapplication of the described method to the image data sets underlyingthe distribution 8, in other words their adaptation by subtraction ofthe respective, individually determined offset.

By adapting the image data sets according to the described method, it isnow possible to define the differentiation threshold value 11 as anabsoluter HU value in the adapted distribution 10, by which the datapoints pertaining to the first tissue type may be clearly distinguishedor separated from the data points pertaining to the tissue type. Theadapted distribution 10 shows the distribution 8 of the HU values fromFIG. 5 after the normalization of the HU values of the underlying imagedata sets. In the example, the differentiation threshold value 11 ischosen such that it lies centrally between the largest value pertainingto the first tissue type and the smallest value pertaining to the secondtissue type and in the present example is 12 HU.

In a method step S11 an infarct core volume given, e.g. mapped, in theadapted image data set is automatically calculated by the dataprocessing facility using the differentiation threshold value 11. Pixelswhose adapted or normalized HU values are smaller than thedifferentiation threshold value 11 are ignored.

Overall, the described examples show how a novel and fully automaticmethod may be implemented in order to reliably correct individualdifferences in X-ray image data sets or X-ray scans. Reliable, in otherwords consistent and comparable, absolute HU values are determined inthe process, providing an application of algorithms, that use HUthreshold values in the soft tissue region, for example for DynaCT, e.g.for software for automatic calculation of infarct core volumes.

It is to be understood that the elements and features recited in theappended claims may be combined in different ways to produce new claimsthat likewise fall within the scope of the present invention. Thus,whereas the dependent claims appended below depend from only a singleindependent or dependent claim, it is to be understood that thesedependent claims may, alternatively, be made to depend in thealternative from any preceding or following claim, whether independentor dependent, and that such new combinations are to be understood asforming a part of the present specification.

While the present invention has been described above by reference tovarious embodiments, it may be understood that many changes andmodifications may be made to the described embodiments. It is thereforeintended that the foregoing description be regarded as illustrativerather than limiting, and that it be understood that all equivalentsand/or combinations of embodiments are intended to be included in thisdescription.

1. A method for automatically adapting an image data set obtained by anX-ray device, that maps a target object, the method comprising:acquiring the image data set comprising a plurality of single imagesthat map different layers of the target object; generating a respectivebinary mask for each of the single images using at least one specifiedthreshold value, wherein the respective binary mask differentiatesdifferent components of the target object in the single images from eachother; determining, using all of the binary masks, one of the singleimages that contains a largest cohesive region of one of the componentsas a reference image; determining a mean value of all pixel values ofthe reference image pertaining to the largest cohesive region;determining an offset from the determined mean value and a specifiedtarget value such that by applying the offset to the pixel values usedfor determining the mean value, the pixel values are adapted such that amean value of the pixel values adapted by the offset corresponds to thespecified target value; and applying the determined offset to all pixelvalues of the image data set.
 2. The method of claim 1, whereindetermining the reference image comprises: determining a respectivelargest cohesive region for each of the binary masks; and comparing thenthe respective largest cohesive regions of all of the binary masks witheach other.
 3. The method of claim 1, wherein two different CT numbersare specified as threshold values for generating the binary masks, bywhich air is differentiated from soft tissue and soft tissue isdifferentiated from bone, wherein for generating the binary masks fromthe single images, for pixel values lying between the two different CTnumbers, a first value is allocated to the binary mask, and for pixelvalues, that lie below the smaller or above the larger of the twodifferent CT numbers, a second value is allocated to the binary mask. 4.The method of claim 1, wherein a low-pass filter is applied to the imagedata set before generation of the binary masks.
 5. The method of claim1, wherein after generation of the binary masks, an erosion operation isfirstly applied to the binary masks before the reference image isdetermined, wherein due to the erosion operation, values of isolatedpixels in each of the binary masks are set at the other binary maskvalue respectively.
 6. The method of claim 1, wherein for determiningthe offset, the specified target value is subtracted from the determinedmean value and for adapting the image data set, the determined offset issubtracted from all pixel values of the image data set.
 7. The method ofclaim 1, wherein the target object is automatically recognized by anobject recognition algorithm, and as a function of the recognized targetobject, the target value for determining the offset is automaticallyselected from a plurality of target values specified for differenttarget objects.
 8. The method of claim 1, wherein that using the adaptedimage data set and at least one likewise correspondingly adapted furtherimage data set that map similar target objects, a differentiationthreshold value is automatically determined that provides a cleardifferentiation of two components of the target objects.
 9. The methodof claim 8, further comprising: mapping a head of a patient as thetarget object using the image data set; providing the adapted image dataset and the differentiation threshold value as input data to an imageprocessing algorithm; and determining an infarct core volume present inthe head using the image processing algorithm.
 10. A non-transitorycomputer implemented storage medium that stores machine-readableinstructions executable by at least one processor for automaticallyadapting an image data set obtained by an X-ray device, themachine-readable instructions comprising: acquiring an image data setcomprising a plurality of single images that map different layers of thetarget object; generating a respective binary mask for each of thesingle images using at least one specified threshold value, wherein therespective binary mask differentiates different components of the targetobject in the single images from each other; determining, using all ofthe binary masks, one of the single images that contains a largestcohesive region of one of the components as a reference image;determining a mean value of all pixel values of the reference imagepertaining to the largest cohesive region; determining an offset fromthe determined mean value and a specified target value such that byapplying the offset to the pixel values used for determining the meanvalue, the pixel values are adapted such that a mean value of the pixelvalues adapted by the offset corresponds to the specified target value;and applying the determined offset to all pixel values of the image dataset.
 11. The non-transitory computer implemented storage medium of claim10, wherein determining the reference image comprises: determining arespective largest cohesive region for each of the binary masks; andcomparing then the respective largest cohesive regions of all of thebinary masks with each other.
 12. The non-transitory computerimplemented storage medium of claim 10, wherein two different CT numbersare specified as threshold values for generating the binary masks, bywhich air is differentiated from soft tissue and soft tissue isdifferentiated from bone, wherein for generating the binary masks fromthe single images, for pixel values lying between the two different CTnumbers, a first value is allocated to the binary mask, and for pixelvalues, that lie below the smaller or above the larger of the twodifferent CT numbers, a second value is allocated to the binary mask.13. The non-transitory computer implemented storage medium of claim 10,wherein a low-pass filter is applied to the image data set beforegeneration of the binary masks.
 14. The non-transitory computerimplemented storage medium of claim 10, wherein after generation of thebinary masks, an erosion operation is firstly applied to the binarymasks before the reference image is determined, wherein due to theerosion operation, values of isolated pixels in each of the binary masksare set at the other binary mask value respectively.
 15. Thenon-transitory computer implemented storage medium of claim 10, whereinfor determining the offset, the specified target value is subtractedfrom the determined mean value and for adapting the image data set, thedetermined offset is subtracted from all pixel values of the image dataset.
 16. The non-transitory computer implemented storage medium of claim10, wherein the target object is automatically recognized by an objectrecognition algorithm, and as a function of the recognized targetobject, the target value for determining the offset is automaticallyselected from a plurality of target values specified for differenttarget objects.
 17. The non-transitory computer implemented storagemedium of claim 10, wherein that using the adapted image data set and atleast one likewise correspondingly adapted further image data set thatmap similar target objects, a differentiation threshold value isautomatically determined that provides a clear differentiation of twocomponents of the target objects.
 18. An X-ray device comprising: anacquisition facility configured to acquire a plurality of single imagesof a target object that map different slices of the target object andtogether form an image data set; and a data processor configured to:generate a respective binary mask for each of the single images using atleast one specified threshold value, wherein the respective binary maskdifferentiates different components of the target object in the singleimages from each other; determine, using all of the binary masks, one ofthe single images that contains a largest cohesive region of one of thecomponents as a reference image; determine a mean value of all pixelvalues of the reference image pertaining to the largest cohesive region;determine an offset from the determined mean value and a specifiedtarget value such that by applying the offset to the pixel values usedfor determining the mean value, the pixel values are adapted such that amean value of the pixel values adapted by the offset corresponds to thespecified target value; and apply the determined offset to all pixelvalues of the image data set.